In public transportation, such as bus or train modes of transport, schedule adherence is important (for both transit agencies and passengers) and refers to the level of success of the service remaining on the intended schedule. The quality of schedule adherence May depend in part on organization and visualization of copious amounts of data relating to transit vehicle performance. A MarEy graph is an example of a visualization tool currently being used for distilling the schedule of a transit system into a single image, however it is still primarily used for viewing schedules, and not performance.
However, since the original publication of the Marey graph in 1885 that plots vehicle paths as an intuitive space-time diagram, visualization has changed little in the depiction of public transit's on-time performance. Partly due to the maturing of GPS technologies, transit visualization techniques have been migrated mostly to points on a map, showing real-time movement and an abundance of other information. However, the majority of these types of visuals do not aid the user in gaining further insight or making judgments about aggregate properties of the data.
Moreover, the Marey graph has remained primarily static, leaving out exploratory properties that can be developed by allowing more interactions with the user. Thus, while transit operators can easily obtain current and historical operation information related to a vehicle or a fleet of vehicles, the information only shows an overall trend of the data, not individual data related to specific incidents that may occur during the operation of a vehicle. For example, the historical information may show how well a vehicle adhered to a set schedule over a period of time (e.g., three months), but the information does not provide an easy way to determine cause of unreliability and the relationship between reliability and passenger travel behavior.